Localization based on predefined features of the environment

ABSTRACT

The subject matter described in this specification is directed to a computer system and techniques for determining a location of an autonomous vehicle. The computer system is configured to determine the location using localization data from multiple data sources. When the localization data from the multiple data sources is unavailable or inaccurate, the computer system is configured to determine the location using predefined features of the environment.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication No. 62/885,629, filed Aug. 12, 2019, entitled “LOCALIZATIONBASED ON PREDEFINED FEATURES OF THE ENVIRONMENT,” the entire contents ofwhich are hereby incorporated by reference.

FIELD

This description relates to autonomous vehicles, and more specificallyto autonomous vehicle localization.

BACKGROUND

Autonomous vehicles can be used to transport people and/or cargo (e.g.,packages, objects, or other items) from one location to another. Forexample, an autonomous vehicle can navigate to the location of a person,wait for the person to board the autonomous vehicle, and navigate to aspecified destination (e.g., a location selected by the person). Tonavigate in the environment, these autonomous vehicles are equipped withvarious types of sensors to detect objects in the surroundings.

SUMMARY

The subject matter described in this specification is directed to asystem and techniques for determining a location of an autonomousvehicle. Generally, the system is configured to determine the locationusing localization data from multiple sources (e.g., GPS and LiDAR).When the localization data from the multiple sources is unavailable orinaccurate (such as in a tunnel or on a bridge), the system isconfigured to determine the location using predefined features of theenvironment.

In particular, an example technique includes: while in a firstlocalization mode: receiving localization data; determining whether thereceived localization data satisfies an accuracy criterion; inaccordance with a determination that the received localization datasatisfies the accuracy criterion: providing an estimated location of avehicle based at least in part on the received localization data; andremaining in the first localization mode; and in accordance with adetermination that the received localization data does not satisfy theaccuracy criterion: switching to a second localization mode; while inthe second localization mode: detecting one or more predefined featuresof the environment surrounding the vehicle; and providing the estimatedlocation of the vehicle based on the one or more predefined features ofthe environment surrounding the vehicle.

These and other aspects, features, and implementations can be expressedas methods, apparatuses, systems, components, program products, means orsteps for performing a function, and in other ways.

These and other aspects, features, and implementations will becomeapparent from the following descriptions, including the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows an example of an autonomous vehicle having autonomouscapability.

FIG. 2 illustrates an example “cloud” computing environment.

FIG. 3 illustrates a computer system.

FIG. 4 shows an example architecture for an autonomous vehicle.

FIG. 5 shows an example of inputs and outputs that may be used by aperception module.

FIG. 6 shows an example of a LiDAR system.

FIG. 7 shows the LiDAR system in operation.

FIG. 8 shows the operation of the LiDAR system in additional detail.

FIG. 9 shows a block diagram of the relationships between inputs andoutputs of a planning module.

FIG. 10 shows a directed graph used in path planning.

FIG. 11 shows a block diagram of the inputs and outputs of a controlmodule.

FIG. 12 shows a block diagram of the inputs, outputs, and components ofa controller.

FIG. 13 is a flow chart of an example process for providing a locationof an autonomous vehicle.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present disclosure. It will be apparent, however,that the disclosed techniques may be practiced without these specificdetails. In other instances, well-known structures and devices are shownin block diagram form in order to avoid unnecessarily obscuring thedisclosed techniques.

In the drawings, specific arrangements or orderings of schematicelements, such as those representing devices, modules, instructionblocks and data elements, are shown for ease of description. However, itshould be understood by those skilled in the art that the specificordering or arrangement of the schematic elements in the drawings is notmeant to imply that a particular order or sequence of processing, orseparation of processes, is required. Further, the inclusion of aschematic element in a drawing is not meant to imply that such elementis required in all embodiments or that the features represented by suchelement may not be included in or combined with other elements in someembodiments.

Further, in the drawings, where connecting elements, such as solid ordashed lines or arrows, are used to illustrate a connection,relationship, or association between or among two or more otherschematic elements, the absence of any such connecting elements is notmeant to imply that no connection, relationship, or association canexist. In other words, some connections, relationships, or associationsbetween elements are not shown in the drawings so as not to obscure thedisclosure. In addition, for ease of illustration, a single connectingelement is used to represent multiple connections, relationships orassociations between elements. For example, where a connecting elementrepresents a communication of signals, data, or instructions, it shouldbe understood by those skilled in the art that such element representsone or multiple signal paths (e.g., a bus), as may be needed, to affectthe communication.

Reference will now be made in detail to embodiments, examples of whichare illustrated in the accompanying drawings. In the following detaileddescription, numerous specific details are set forth in order to providea thorough understanding of the various described embodiments. However,it will be apparent to one of ordinary skill in the art that the variousdescribed embodiments may be practiced without these specific details.In other instances, well-known methods, procedures, components,circuits, and networks have not been described in detail so as not tounnecessarily obscure aspects of the embodiments.

Several features are described hereafter that can each be usedindependently of one another or with any combination of other features.However, any individual feature may not address any of the problemsdiscussed above or might only address one of the problems discussedabove. Some of the problems discussed above might not be fully addressedby any of the features described herein. Although headings are provided,information related to a particular heading, but not found in thesection having that heading, may also be found elsewhere in thisdescription. Embodiments are described herein according to the followingoutline:

-   -   1. General Overview    -   2. Hardware Overview    -   3. Autonomous Vehicle Architecture    -   4. Autonomous Vehicle Inputs    -   5. Autonomous Vehicle Planning    -   6. Autonomous Vehicle Control    -   7. Computing System for Object Detection Using Pillars    -   8. Example Point Clouds and Pillars    -   9. Example Process for Detecting Objects and Operating the        Vehicle Based on the Detection of the Objects

General Overview

Autonomous vehicles driving in complex environments (e.g., an urbanenvironment) pose a great technological challenge. In order for anautonomous vehicle to navigate these environments, the vehicle detectsvarious types of objects such as vehicles, pedestrians, and bikes inreal-time using sensors such as LiDAR or RADAR. The autonomous vehiclealso determines the current location of the vehicle using localizationdata, often from multiple sources such as GPS and LiDAR. When thelocalization data from the multiple sources all indicate the sameapproximate location for the vehicle, then the vehicle can operate witha high confidence that the location indicated by the localization datais accurate. However, when the localization data from the multiplesources is unavailable or inaccurate (such as in a tunnel or on abridge), the autonomous vehicle is configured to determine the currentlocation using other sources.

In particular, the system and techniques described herein provide anestimated location of an autonomous vehicle based on predefined featuresof the environment, such as the textural pattern of the road surface orother markers installed along the roadway. The autonomous vehicleswitches to a localization mode that uses the predefined features of theenvironment when the localization data from other sources (such as GPSor LiDAR) fail to meet an accuracy criterion, such as when localizationdata is only available from one source (e.g., when no GPS signal isdetected).

Hardware Overview

FIG. 1 shows an example of an autonomous vehicle 100 having autonomouscapability.

As used herein, the term “autonomous capability” refers to a function,feature, or facility that enables a vehicle to be partially or fullyoperated without real-time human intervention, including withoutlimitation fully autonomous vehicles, highly autonomous vehicles, andconditionally autonomous vehicles.

As used herein, an autonomous vehicle (AV) is a vehicle that possessesautonomous capability.

As used herein, “vehicle” includes means of transportation of goods orpeople. For example, cars, buses, trains, airplanes, drones, trucks,boats, ships, submersibles, dirigibles, etc. A driverless car is anexample of a vehicle.

As used herein, “trajectory” refers to a path or route to navigate an AVfrom a first spatiotemporal location to second spatiotemporal location.In an embodiment, the first spatiotemporal location is referred to asthe initial or starting location and the second spatiotemporal locationis referred to as the destination, final location, goal, goal position,or goal location. In some examples, a trajectory is made up of one ormore segments (e.g., sections of road) and each segment is made up ofone or more blocks (e.g., portions of a lane or intersection). In anembodiment, the spatiotemporal locations correspond to real worldlocations. For example, the spatiotemporal locations are pick up ordrop-off locations to pick up or drop-off persons or goods.

As used herein, “sensor(s)” includes one or more hardware componentsthat detect information about the environment surrounding the sensor.Some of the hardware components can include sensing components (e.g.,image sensors, biometric sensors), transmitting and/or receivingcomponents (e.g., laser or radio frequency wave transmitters andreceivers), electronic components such as analog-to-digital converters,a data storage device (such as a RAM and/or a nonvolatile storage),software or firmware components and data processing components such asan ASIC (application-specific integrated circuit), a microprocessorand/or a microcontroller.

As used herein, a “scene description” is a data structure (e.g., list)or data stream that includes one or more classified or labeled objectsdetected by one or more sensors on the AV vehicle or provided by asource external to the AV.

As used herein, a “road” is a physical area that can be traversed by avehicle, and may correspond to a named thoroughfare (e.g., city street,interstate freeway, etc.) or may correspond to an unnamed thoroughfare(e.g., a driveway in a house or office building, a section of a parkinglot, a section of a vacant lot, a dirt path in a rural area, etc.).Because some vehicles (e.g., 4-wheel-drive pickup trucks, sport utilityvehicles, etc.) are capable of traversing a variety of physical areasnot specifically adapted for vehicle travel, a “road” may be a physicalarea not formally defined as a thoroughfare by any municipality or othergovernmental or administrative body.

As used herein, a “lane” is a portion of a road that can be traversed bya vehicle, and may correspond to most or all of the space between lanemarkings, or may correspond to only some (e.g., less than 50%) of thespace between lane markings. For example, a road having lane markingsspaced far apart might accommodate two or more vehicles between themarkings, such that one vehicle can pass the other without traversingthe lane markings, and thus could be interpreted as having a lanenarrower than the space between the lane markings, or having two lanesbetween the lane markings. A lane could also be interpreted in theabsence of lane markings. For example, a lane may be defined based onphysical features of an environment, e.g., rocks and trees along athoroughfare in a rural area.

“One or more” includes a function being performed by one element, afunction being performed by more than one element, e.g., in adistributed fashion, several functions being performed by one element,several functions being performed by several elements, or anycombination of the above.

It will also be understood that, although the terms first, second, etc.are, in some instances, used herein to describe various elements, theseelements should not be limited by these terms. These terms are only usedto distinguish one element from another. For example, a first contactcould be termed a second contact, and, similarly, a second contact couldbe termed a first contact, without departing from the scope of thevarious described embodiments. The first contact and the second contactare both contacts, but they are not the same contact.

The terminology used in the description of the various describedembodiments herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used in thedescription of the various described embodiments and the appendedclaims, the singular forms “a,” “an” and “the” are intended to includethe plural forms as well, unless the context clearly indicatesotherwise. It will also be understood that the term “and/or” as usedherein refers to and encompasses any and all possible combinations ofone or more of the associated listed items. It will be furtherunderstood that the terms “includes,” “including,” “comprises,” and/or“comprising,” when used in this description, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

As used herein, the term “if” is, optionally, construed to mean “when”or “upon” or “in response to determining” or “in response to detecting,”depending on the context. Similarly, the phrase “if it is determined” or“if [a stated condition or event] is detected” is, optionally, construedto mean “upon determining” or “in response to determining” or “upondetecting [the stated condition or event]” or “in response to detecting[the stated condition or event],” depending on the context.

As used herein, an AV system refers to the AV along with the array ofhardware, software, stored data, and data generated in real-time thatsupports the operation of the AV. In an embodiment, the AV system isincorporated within the AV. In an embodiment, the AV system is spreadacross several locations. For example, some of the software of the AVsystem is implemented on a cloud computing environment similar to cloudcomputing environment 300 described below with respect to FIG. 3 .

In general, this document describes technologies applicable to anyvehicles that have one or more autonomous capabilities including fullyautonomous vehicles, highly autonomous vehicles, and conditionallyautonomous vehicles, such as so-called Level 5, Level 4 and Level 3vehicles, respectively (see SAE International's standard J3016: Taxonomyand Definitions for Terms Related to On-Road Motor Vehicle AutomatedDriving Systems, which is incorporated by reference in its entirety, formore details on the classification of levels of autonomy in vehicles).The technologies described in this document are also applicable topartially autonomous vehicles and driver assisted vehicles, such asso-called Level 2 and Level 1 vehicles (see SAE International's standardJ3016: Taxonomy and Definitions for Terms Related to On-Road MotorVehicle Automated Driving Systems). In an embodiment, one or more of theLevel 1, 2, 3, 4 and 5 vehicle systems may automate certain vehicleoperations (e.g., steering, braking, and using maps) under certainoperating conditions based on processing of sensor inputs. Thetechnologies described in this document can benefit vehicles in anylevels, ranging from fully autonomous vehicles to human-operatedvehicles.

Referring to FIG. 1 , an AV system 120 operates the AV 100 along atrajectory 198 through an environment 190 to a destination 199(sometimes referred to as a final location) while avoiding objects(e.g., natural obstructions 191, vehicles 193, pedestrians 192,cyclists, and other obstacles) and obeying rules of the road (e.g.,rules of operation or driving preferences).

In an embodiment, the AV system 120 includes devices 101 that areinstrumented to receive and act on operational commands from thecomputer processors 146. In an embodiment, computing processors 146 aresimilar to the processor 304 described below in reference to FIG. 3 .Examples of devices 101 include a steering control 102, brakes 103,gears, accelerator pedal or other acceleration control mechanisms,windshield wipers, side-door locks, window controls, andturn-indicators.

In an embodiment, the AV system 120 includes sensors 121 for measuringor inferring properties of state or condition of the AV 100, such as theAV's position, linear and angular velocity and acceleration, and heading(e.g., an orientation of the leading end of AV 100). Example of sensors121 are GPS, inertial measurement units (IMU) that measure both vehiclelinear accelerations and angular rates, wheel speed sensors formeasuring or estimating wheel slip ratios, wheel brake pressure orbraking torque sensors, engine torque or wheel torque sensors, andsteering angle and angular rate sensors.

In an embodiment, the sensors 121 also include sensors for sensing ormeasuring properties of the AV's environment. For example, monocular orstereo video cameras 122 in the visible light, infrared or thermal (orboth) spectra, LiDAR 123, RADAR, ultrasonic sensors, time-of-flight(TOF) depth sensors, speed sensors, temperature sensors, humiditysensors, and precipitation sensors.

In an embodiment, the AV system 120 includes a data storage unit 142 andmemory 144 for storing machine instructions associated with computerprocessors 146 or data collected by sensors 121. In an embodiment, thedata storage unit 142 is similar to the ROM 308 or storage device 310described below in relation to FIG. 3 . In an embodiment, memory 144 issimilar to the main memory 306 described below. In an embodiment, thedata storage unit 142 and memory 144 store historical, real-time, and/orpredictive information about the environment 190. In an embodiment, thestored information includes maps, driving performance, trafficcongestion updates or weather conditions. In an embodiment, datarelating to the environment 190 is transmitted to the AV 100 via acommunications channel from a remotely located database 134.

In an embodiment, the AV system 120 includes communications devices 140for communicating measured or inferred properties of other vehicles'states and conditions, such as positions, linear and angular velocities,linear and angular accelerations, and linear and angular headings to theAV 100. These devices include Vehicle-to-Vehicle (V2V) andVehicle-to-Infrastructure (V2I) communication devices and devices forwireless communications over point-to-point or ad hoc networks or both.In an embodiment, the communications devices 140 communicate across theelectromagnetic spectrum (including radio and optical communications) orother media (e.g., air and acoustic media). A combination ofVehicle-to-Vehicle (V2V) Vehicle-to-Infrastructure (V2I) communication(and, in some embodiments, one or more other types of communication) issometimes referred to as Vehicle-to-Everything (V2X) communication. V2Xcommunication typically conforms to one or more communications standardsfor communication with, between, and among autonomous vehicles.

In an embodiment, the communication devices 140 include communicationinterfaces. For example, wired, wireless, WiMAX, WiFi, Bluetooth,satellite, cellular, optical, near field, infrared, or radio interfaces.The communication interfaces transmit data from a remotely locateddatabase 134 to AV system 120. In an embodiment, the remotely locateddatabase 134 is embedded in a cloud computing environment 200 asdescribed in FIG. 2 . The communication interfaces 140 transmit datacollected from sensors 121 or other data related to the operation of AV100 to the remotely located database 134. In an embodiment,communication interfaces 140 transmit information that relates toteleoperations to the AV 100. In some embodiments, the AV 100communicates with other remote (e.g., “cloud”) servers 136.

In an embodiment, the remotely located database 134 also stores andtransmits digital data (e.g., storing data such as road and streetlocations). Such data is stored on the memory 144 on the AV 100, ortransmitted to the AV 100 via a communications channel from the remotelylocated database 134.

In an embodiment, the remotely located database 134 stores and transmitshistorical information about driving properties (e.g., speed andacceleration profiles) of vehicles that have previously traveled alongtrajectory 198 at similar times of day. In one implementation, such datamay be stored on the memory 144 on the AV 100, or transmitted to the AV100 via a communications channel from the remotely located database 134.

Computing devices 146 located on the AV 100 algorithmically generatecontrol actions based on both real-time sensor data and priorinformation, allowing the AV system 120 to execute its autonomousdriving capabilities.

In an embodiment, the AV system 120 includes computer peripherals 132coupled to computing devices 146 for providing information and alertsto, and receiving input from, a user (e.g., an occupant or a remoteuser) of the AV 100. In an embodiment, peripherals 132 are similar tothe display 312, input device 314, and cursor controller 316 discussedbelow in reference to FIG. 3 . The coupling is wireless or wired. Anytwo or more of the interface devices may be integrated into a singledevice.

FIG. 2 illustrates an example “cloud” computing environment. Cloudcomputing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services). Intypical cloud computing systems, one or more large cloud data centershouse the machines used to deliver the services provided by the cloud.Referring now to FIG. 2 , the cloud computing environment 200 includescloud data centers 204 a, 204 b, and 204 c that are interconnectedthrough the cloud 202. Data centers 204 a, 204 b, and 204 c providecloud computing services to computer systems 206 a, 206 b, 206 c, 206 d,206 e, and 206 f connected to cloud 202.

The cloud computing environment 200 includes one or more cloud datacenters. In general, a cloud data center, for example the cloud datacenter 204 a shown in FIG. 2 , refers to the physical arrangement ofservers that make up a cloud, for example the cloud 202 shown in FIG. 2, or a particular portion of a cloud. For example, servers arephysically arranged in the cloud datacenter into rooms, groups, rows,and racks. A cloud datacenter has one or more zones, which include oneor more rooms of servers. Each room has one or more rows of servers, andeach row includes one or more racks. Each rack includes one or moreindividual server nodes. In some implementation, servers in zones,rooms, racks, and/or rows are arranged into groups based on physicalinfrastructure requirements of the datacenter facility, which includepower, energy, thermal, heat, and/or other requirements. In anembodiment, the server nodes are similar to the computer systemdescribed in FIG. 3 . The data center 204 a has many computing systemsdistributed through many racks.

The cloud 202 includes cloud data centers 204 a, 204 b, and 204 c alongwith the network and networking resources (for example, networkingequipment, nodes, routers, switches, and networking cables) thatinterconnect the cloud data centers 204 a, 204 b, and 204 c and helpfacilitate the computing systems' 206 a-f access to cloud computingservices. In an embodiment, the network represents any combination ofone or more local networks, wide area networks, or internetworks coupledusing wired or wireless links deployed using terrestrial or satelliteconnections. Data exchanged over the network, is transferred using anynumber of network layer protocols, such as Internet Protocol (IP),Multiprotocol Label Switching (MPLS), Asynchronous Transfer Mode (ATM),Frame Relay, etc. Furthermore, in embodiments where the networkrepresents a combination of multiple sub-networks, different networklayer protocols are used at each of the underlying sub-networks. In someembodiments, the network represents one or more interconnectedinternetworks, such as the public Internet.

The computing systems 206 a-f or cloud computing services consumers areconnected to the cloud 202 through network links and network adapters.In an embodiment, the computing systems 206 a-f are implemented asvarious computing devices, for example servers, desktops, laptops,tablet, smartphones, Internet of Things (IoT) devices, autonomousvehicles (including, cars, drones, shuttles, trains, buses, etc.) andconsumer electronics. In an embodiment, the computing systems 206 a-fare implemented in or as a part of other systems.

FIG. 3 illustrates a computer system 300. In an implementation, thecomputer system 300 is a special purpose computing device. Thespecial-purpose computing device is hard-wired to perform the techniquesor includes digital electronic devices such as one or moreapplication-specific integrated circuits (ASICs) or field programmablegate arrays (FPGAs) that are persistently programmed to perform thetechniques, or may include one or more general purpose hardwareprocessors programmed to perform the techniques pursuant to programinstructions in firmware, memory, other storage, or a combination. Suchspecial-purpose computing devices may also combine custom hard-wiredlogic, ASICs, or FPGAs with custom programming to accomplish thetechniques. In various embodiments, the special-purpose computingdevices are desktop computer systems, portable computer systems,handheld devices, network devices or any other device that incorporateshard-wired and/or program logic to implement the techniques.

In an embodiment, the computer system 300 includes a bus 302 or othercommunication mechanism for communicating information, and a hardwareprocessor 304 coupled with a bus 302 for processing information. Thehardware processor 304 is, for example, a general-purposemicroprocessor. The computer system 300 also includes a main memory 306,such as a random-access memory (RAM) or other dynamic storage device,coupled to the bus 302 for storing information and instructions to beexecuted by processor 304. In one implementation, the main memory 306 isused for storing temporary variables or other intermediate informationduring execution of instructions to be executed by the processor 304.Such instructions, when stored in non-transitory storage mediaaccessible to the processor 304, render the computer system 300 into aspecial-purpose machine that is customized to perform the operationsspecified in the instructions.

In an embodiment, the computer system 300 further includes a read onlymemory (ROM) 308 or other static storage device coupled to the bus 302for storing static information and instructions for the processor 304. Astorage device 310, such as a magnetic disk, optical disk, solid-statedrive, or three-dimensional cross point memory is provided and coupledto the bus 302 for storing information and instructions.

In an embodiment, the computer system 300 is coupled via the bus 302 toa display 312, such as a cathode ray tube (CRT), a liquid crystaldisplay (LCD), plasma display, light emitting diode (LED) display, or anorganic light emitting diode (OLED) display for displaying informationto a computer user. An input device 314, including alphanumeric andother keys, is coupled to bus 302 for communicating information andcommand selections to the processor 304. Another type of user inputdevice is a cursor controller 316, such as a mouse, a trackball, atouch-enabled display, or cursor direction keys for communicatingdirection information and command selections to the processor 304 andfor controlling cursor movement on the display 312. This input devicetypically has two degrees of freedom in two axes, a first axis (e.g.,x-axis) and a second axis (e.g., y-axis), that allows the device tospecify positions in a plane.

According to one embodiment, the techniques herein are performed by thecomputer system 300 in response to the processor 304 executing one ormore sequences of one or more instructions contained in the main memory306. Such instructions are read into the main memory 306 from anotherstorage medium, such as the storage device 310. Execution of thesequences of instructions contained in the main memory 306 causes theprocessor 304 to perform the process steps described herein. Inalternative embodiments, hard-wired circuitry is used in place of or incombination with software instructions.

The term “storage media” as used herein refers to any non-transitorymedia that store data and/or instructions that cause a machine tooperate in a specific fashion. Such storage media includes non-volatilemedia and/or volatile media. Non-volatile media includes, for example,optical disks, magnetic disks, solid-state drives, or three-dimensionalcross point memory, such as the storage device 310. Volatile mediaincludes dynamic memory, such as the main memory 306. Common forms ofstorage media include, for example, a floppy disk, a flexible disk, harddisk, solid-state drive, magnetic tape, or any other magnetic datastorage medium, a CD-ROM, any other optical data storage medium, anyphysical medium with patterns of holes, a RAM, a PROM, and EPROM, aFLASH-EPROM, NV-RAM, or any other memory chip or cartridge.

Storage media is distinct from but may be used in conjunction withtransmission media. Transmission media participates in transferringinformation between storage media. For example, transmission mediaincludes coaxial cables, copper wire and fiber optics, including thewires that comprise the bus 302. Transmission media can also take theform of acoustic or light waves, such as those generated duringradio-wave and infrared data communications.

In an embodiment, various forms of media are involved in carrying one ormore sequences of one or more instructions to the processor 304 forexecution. For example, the instructions are initially carried on amagnetic disk or solid-state drive of a remote computer. The remotecomputer loads the instructions into its dynamic memory and send theinstructions over a telephone line using a modem. A modem local to thecomputer system 300 receives the data on the telephone line and use aninfrared transmitter to convert the data to an infrared signal. Aninfrared detector receives the data carried in the infrared signal andappropriate circuitry places the data on the bus 302. The bus 302carries the data to the main memory 306, from which processor 304retrieves and executes the instructions. The instructions received bythe main memory 306 may optionally be stored on the storage device 310either before or after execution by processor 304.

The computer system 300 also includes a communication interface 318coupled to the bus 302. The communication interface 318 provides atwo-way data communication coupling to a network link 320 that isconnected to a local network 322. For example, the communicationinterface 318 is an integrated service digital network (ISDN) card,cable modem, satellite modem, or a modem to provide a data communicationconnection to a corresponding type of telephone line. As anotherexample, the communication interface 318 is a local area network (LAN)card to provide a data communication connection to a compatible LAN. Insome implementations, wireless links are also implemented. In any suchimplementation, the communication interface 318 sends and receiveselectrical, electromagnetic, or optical signals that carry digital datastreams representing various types of information.

The network link 320 typically provides data communication through oneor more networks to other data devices. For example, the network link320 provides a connection through the local network 322 to a hostcomputer 324 or to a cloud data center or equipment operated by anInternet Service Provider (ISP) 326. The ISP 326 in turn provides datacommunication services through the world-wide packet data communicationnetwork now commonly referred to as the “Internet” 328. The localnetwork 322 and Internet 328 both use electrical, electromagnetic oroptical signals that carry digital data streams. The signals through thevarious networks and the signals on the network link 320 and through thecommunication interface 318, which carry the digital data to and fromthe computer system 300, are example forms of transmission media. In anembodiment, the network 320 contains the cloud 202 or a part of thecloud 202 described above.

The computer system 300 sends messages and receives data, includingprogram code, through the network(s), the network link 320, and thecommunication interface 318. In an embodiment, the computer system 300receives code for processing. The received code is executed by theprocessor 304 as it is received, and/or stored in storage device 310, orother non-volatile storage for later execution.

Autonomous Vehicle Architecture

FIG. 4 shows an example architecture 400 for an autonomous vehicle(e.g., the AV 100 shown in FIG. 1 ). The architecture 400 includes aperception module 402 (sometimes referred to as a perception circuit), aplanning module 404 (sometimes referred to as a planning circuit), acontrol module 406 (sometimes referred to as a control circuit), alocalization module 408 (sometimes referred to as a localizationcircuit), and a database module 410 (sometimes referred to as a databasecircuit). Each module plays a role in the operation of the AV 100.Together, the modules 402, 404, 406, 408, and 410 may be part of the AVsystem 120 shown in FIG. 1 . In some embodiments, any of the modules402, 404, 406, 408, and 410 is a combination of computer software (e.g.,executable code stored on a computer-readable medium) and computerhardware (e.g., one or more microprocessors, microcontrollers,application-specific integrated circuits [ASICs]), hardware memorydevices, other types of integrated circuits, other types of computerhardware, or a combination of any or all of these things).

In use, the planning module 404 receives data representing a destination412 and determines data representing a trajectory 414 (sometimesreferred to as a route) that can be traveled by the AV 100 to reach(e.g., arrive at) the destination 412. In order for the planning module404 to determine the data representing the trajectory 414, the planningmodule 404 receives data from the perception module 402, thelocalization module 408, and the database module 410.

The perception module 402 identifies nearby physical objects using oneor more sensors 121, e.g., as also shown in FIG. 1 . The objects areclassified (e.g., grouped into types such as pedestrian, bicycle,automobile, traffic sign, etc.) and a scene description including theclassified objects 416 is provided to the planning module 404.

The planning module 404 also receives data representing the AV position418 from the localization module 408. The localization module 408determines the AV position by using data from the sensors 121 and datafrom the database module 410 (e.g., a geographic data) to calculate aposition. For example, the localization module 408 uses data from a GNSS(Global Navigation Satellite System) sensor and geographic data tocalculate a longitude and latitude of the AV. In an embodiment, dataused by the localization module 408 includes high-precision maps of theroadway geometric properties, maps describing road network connectivityproperties, maps describing roadway physical properties (such as trafficspeed, traffic volume, the number of vehicular and cyclist trafficlanes, lane width, lane traffic directions, or lane marker types andlocations, or combinations of them), and maps describing the spatiallocations of road features such as crosswalks, traffic signs or othertravel signals of various types.

The control module 406 receives the data representing the trajectory 414and the data representing the AV position 418 and operates the controlfunctions 420 a-c (e.g., steering, throttling, braking, ignition) of theAV in a manner that will cause the AV 100 to travel the trajectory 414to the destination 412. For example, if the trajectory 414 includes aleft turn, the control module 406 will operate the control functions 420a-c in a manner such that the steering angle of the steering functionwill cause the AV 100 to turn left and the throttling and braking willcause the AV 100 to pause and wait for passing pedestrians or vehiclesbefore the turn is made.

Autonomous Vehicle Inputs

FIG. 5 shows an example of inputs 502 a-d (e.g., sensors 121 shown inFIG. 1 ) and outputs 504 a-d (e.g., sensor data) that is used by theperception module 402 (FIG. 4 ). One input 502 a is a LiDAR (LightDetection and Ranging) system (e.g., LiDAR 123 shown in FIG. 1 ). LiDARis a technology that uses light (e.g., bursts of light such as infraredlight) to obtain data about physical objects in its line of sight. ALiDAR system produces LiDAR data as output 504 a. For example, LiDARdata is collections of 3D or 2D points (also known as a point clouds)that are used to construct a representation of the environment 190.

Another input 502 b is a RADAR system. RADAR is a technology that usesradio waves to obtain data about nearby physical objects. RADARs canobtain data about objects not within the line of sight of a LiDARsystem. A RADAR system 502 b produces RADAR data as output 504 b. Forexample, RADAR data are one or more radio frequency electromagneticsignals that are used to construct a representation of the environment190.

Another input 502 c is a camera system. A camera system uses one or morecameras (e.g., digital cameras using a light sensor such as acharge-coupled device [CCD]) to obtain information about nearby physicalobjects. A camera system produces camera data as output 504 c. Cameradata often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). In some examples, the camerasystem has multiple independent cameras, e.g., for the purpose ofstereopsis (stereo vision), which enables the camera system to perceivedepth. Although the objects perceived by the camera system are describedhere as “nearby,” this is relative to the AV. In use, the camera systemmay be configured to “see” objects far, e.g., up to a kilometer or moreahead of the AV. Accordingly, the camera system may have features suchas sensors and lenses that are optimized for perceiving objects that arefar away.

Another input 502 d is a traffic light detection (TLD) system. A TLDsystem uses one or more cameras to obtain information about trafficlights, street signs, and other physical objects that provide visualnavigation information. A TLD system produces TLD data as output 504 d.TLD data often takes the form of image data (e.g., data in an image dataformat such as RAW, JPEG, PNG, etc.). A TLD system differs from a systemincorporating a camera in that a TLD system uses a camera with a widefield of view (e.g., using a wide-angle lens or a fish-eye lens) inorder to obtain information about as many physical objects providingvisual navigation information as possible, so that the AV 100 has accessto all relevant navigation information provided by these objects. Forexample, the viewing angle of the TLD system may be about 120 degrees ormore.

In some embodiments, outputs 504 a-d are combined using a sensor fusiontechnique. Thus, either the individual outputs 504 a-d are provided toother systems of the AV 100 (e.g., provided to a planning module 404 asshown in FIG. 4 ), or the combined output can be provided to the othersystems, either in the form of a single combined output or multiplecombined outputs of the same type (e.g., using the same combinationtechnique or combining the same outputs or both) or different types type(e.g., using different respective combination techniques or combiningdifferent respective outputs or both). In some embodiments, an earlyfusion technique is used. An early fusion technique is characterized bycombining outputs before one or more data processing steps are appliedto the combined output. In some embodiments, a late fusion technique isused. A late fusion technique is characterized by combining outputsafter one or more data processing steps are applied to the individualoutputs.

FIG. 6 shows an example of a LiDAR system 602 (e.g., the input 502 ashown in FIG. 5 ). The LiDAR system 602 emits light 604 a-c from a lightemitter 606 (e.g., a laser transmitter). Light emitted by a LiDAR systemis typically not in the visible spectrum; for example, infrared light isoften used. Some of the light 604 b emitted encounters a physical object608 (e.g., a vehicle) and reflects back to the LiDAR system 602. (Lightemitted from a LiDAR system typically does not penetrate physicalobjects, e.g., physical objects in solid form.) The LiDAR system 602also has one or more light detectors 610, which detect the reflectedlight. In an embodiment, one or more data processing systems associatedwith the LiDAR system generates an image 612 representing the field ofview 614 of the LiDAR system. The image 612 includes information thatrepresents the boundaries 616 of a physical object 608. In this way, theimage 612 is used to determine the boundaries 616 of one or morephysical objects near an AV.

FIG. 7 shows the LiDAR system 602 in operation. In the scenario shown inthis figure, the AV 100 receives both camera system output 504 c in theform of an image 702 and LiDAR system output 504 a in the form of LiDARdata points 704. In use, the data processing systems of the AV 100compares the image 702 to the data points 704. In particular, a physicalobject 706 identified in the image 702 is also identified among the datapoints 704. In this way, the AV 100 perceives the boundaries of thephysical object based on the contour and density of the data points 704.

FIG. 8 shows the operation of the LiDAR system 602 in additional detail.As described above, the AV 100 detects the boundary of a physical objectbased on characteristics of the data points detected by the LiDAR system602. As shown in FIG. 8 , a flat object, such as the ground 802, willreflect light 804 a-d emitted from a LiDAR system 602 in a consistentmanner. Put another way, because the LiDAR system 602 emits light usingconsistent spacing, the ground 802 will reflect light back to the LiDARsystem 602 with the same consistent spacing. As the AV 100 travels overthe ground 802, the LiDAR system 602 will continue to detect lightreflected by the next valid ground point 806 if nothing is obstructingthe road. However, if an object 808 obstructs the road, light 804 e-femitted by the LiDAR system 602 will be reflected from points 810 a-b ina manner inconsistent with the expected consistent manner. From thisinformation, the AV 100 can determine that the object 808 is present.

Path Planning

FIG. 9 shows a block diagram 900 of the relationships between inputs andoutputs of a planning module 404 (e.g., as shown in FIG. 4 ). Ingeneral, the output of a planning module 404 is a route 902 from a startpoint 904 (e.g., source location or initial location), and an end point906 (e.g., destination or final location). The route 902 is typicallydefined by one or more segments. For example, a segment is a distance tobe traveled over at least a portion of a street, road, highway,driveway, or other physical area appropriate for automobile travel. Insome examples, e.g., if the AV 100 is an off-road capable vehicle suchas a four-wheel-drive (4WD) or all-wheel-drive (AWD) car, SUV, pick-uptruck, or the like, the route 902 includes “off-road” segments such asunpaved paths or open fields.

In addition to the route 902, a planning module also outputs lane-levelroute planning data 908. The lane-level route planning data 908 is usedto traverse segments of the route 902 based on conditions of the segmentat a particular time. For example, if the route 902 includes amulti-lane highway, the lane-level route planning data 908 includestrajectory planning data 910 that the AV 100 can use to choose a laneamong the multiple lanes, e.g., based on whether an exit is approaching,whether one or more of the lanes have other vehicles, or other factorsthat vary over the course of a few minutes or less. Similarly, in someimplementations, the lane-level route planning data 908 includes speedconstraints 912 specific to a segment of the route 902. For example, ifthe segment includes pedestrians or un-expected traffic, the speedconstraints 912 may limit the AV 100 to a travel speed slower than anexpected speed, e.g., a speed based on speed limit data for the segment.

In an embodiment, the inputs to the planning module 404 includesdatabase data 914 (e.g., from the database module 410 shown in FIG. 4 ),current location data 916 (e.g., the AV position 418 shown in FIG. 4 ),destination data 918 (e.g., for the destination 412 shown in FIG. 4 ),and object data 920 (e.g., the classified objects 416 as perceived bythe perception module 402 as shown in FIG. 4 ). In some embodiments, thedatabase data 914 includes rules used in planning. Rules are specifiedusing a formal language, e.g., using Boolean logic. In any givensituation encountered by the AV 100, at least some of the rules willapply to the situation. A rule applies to a given situation if the rulehas conditions that are met based on information available to the AV100, e.g., information about the surrounding environment. Rules can havepriority. For example, a rule that says, “if the road is a freeway, moveto the leftmost lane” can have a lower priority than “if the exit isapproaching within a mile, move to the rightmost lane.”

FIG. 10 shows a directed graph 1000 used in path planning, e.g., by theplanning module 404 (FIG. 4 ). In general, a directed graph 1000 likethe one shown in FIG. 10 is used to determine a path between any startpoint 1002 and end point 1004. In real-world terms, the distanceseparating the start point 1002 and end point 1004 may be relativelylarge (e.g, in two different metropolitan areas) or may be relativelysmall (e.g., two intersections abutting a city block or two lanes of amulti-lane road).

In an embodiment, the directed graph 1000 has nodes 1006 a-drepresenting different locations between the start point 1002 and theend point 1004 that could be occupied by an AV 100. In some examples,e.g., when the start point 1002 and end point 1004 represent differentmetropolitan areas, the nodes 1006 a-d represent segments of roads. Insome examples, e.g., when the start point 1002 and the end point 1004represent different locations on the same road, the nodes 1006 a-drepresent different positions on that road. In this way, the directedgraph 1000 includes information at varying levels of granularity. In anembodiment, a directed graph having high granularity is also a subgraphof another directed graph having a larger scale. For example, a directedgraph in which the start point 1002 and the end point 1004 are far away(e.g., many miles apart) has most of its information at a lowgranularity and is based on stored data, but also includes some highgranularity information for the portion of the graph that representsphysical locations in the field of view of the AV 100.

The nodes 1006 a-d are distinct from objects 1008 a-b which cannotoverlap with a node. In an embodiment, when granularity is low, theobjects 1008 a-b represent regions that cannot be traversed byautomobile, e.g., areas that have no streets or roads. When granularityis high, the objects 1008 a-b represent physical objects in the field ofview of the AV 100, e.g., other automobiles, pedestrians, or otherentities with which the AV 100 cannot share physical space. In anembodiment, some or all of the objects 1008 a-b are a static objects(e.g., an object that does not change position such as a street lamp orutility pole) or dynamic objects (e.g., an object that is capable ofchanging position such as a pedestrian or other car).

The nodes 1006 a-d are connected by edges 1010 a-c. If two nodes 1006a-b are connected by an edge 1010 a, it is possible for an AV 100 totravel between one node 1006 a and the other node 1006 b, e.g., withouthaving to travel to an intermediate node before arriving at the othernode 1006 b. (When we refer to an AV 100 traveling between nodes, wemean that the AV 100 travels between the two physical positionsrepresented by the respective nodes.) The edges 1010 a-c are oftenbidirectional, in the sense that an AV 100 travels from a first node toa second node, or from the second node to the first node. In anembodiment, edges 1010 a-c are unidirectional, in the sense that an AV100 can travel from a first node to a second node, however the AV 100cannot travel from the second node to the first node. Edges 1010 a-c areunidirectional when they represent, for example, one-way streets,individual lanes of a street, road, or highway, or other features thatcan only be traversed in one direction due to legal or physicalconstraints.

In an embodiment, the planning module 404 uses the directed graph 1000to identify a path 1012 made up of nodes and edges between the startpoint 1002 and end point 1004.

An edge 1010 a-c has an associated cost 1014 a-b. The cost 1014 a-b is avalue that represents the resources that will be expended if the AV 100chooses that edge. A typical resource is time. For example, if one edge1010 a represents a physical distance that is twice that as another edge1010 b, then the associated cost 1014 a of the first edge 1010 a may betwice the associated cost 1014 b of the second edge 1010 b. Otherfactors that affect time include expected traffic, number ofintersections, speed limit, etc. Another typical resource is fueleconomy. Two edges 1010 a-b may represent the same physical distance,but one edge 1010 a may require more fuel than another edge 1010 b,e.g., because of road conditions, expected weather, etc.

When the planning module 404 identifies a path 1012 between the startpoint 1002 and end point 1004, the planning module 404 typically choosesa path optimized for cost, e.g., the path that has the least total costwhen the individual costs of the edges are added together.

Autonomous Vehicle Control

FIG. 11 shows a block diagram 1100 of the inputs and outputs of acontrol module 406 (e.g., as shown in FIG. 4 ). A control moduleoperates in accordance with a controller 1102 which includes, forexample, one or more processors (e.g., one or more computer processorssuch as microprocessors or microcontrollers or both) similar toprocessor 304, short-term and/or long-term data storage (e.g., memoryrandom-access memory or flash memory or both) similar to main memory306, ROM 308, and storage device 310, and instructions stored in memorythat carry out operations of the controller 1102 when the instructionsare executed (e.g., by the one or more processors).

In an embodiment, the controller 1102 receives data representing adesired output 1104. The desired output 1104 typically includes avelocity, e.g., a speed and a heading. The desired output 1104 can bebased on, for example, data received from a planning module 404 (e.g.,as shown in FIG. 4 ). In accordance with the desired output 1104, thecontroller 1102 produces data usable as a throttle input 1106 and asteering input 1108. The throttle input 1106 represents the magnitude inwhich to engage the throttle (e.g., acceleration control) of an AV 100,e.g., by engaging the steering pedal, or engaging another throttlecontrol, to achieve the desired output 1104. In some examples, thethrottle input 1106 also includes data usable to engage the brake (e.g.,deceleration control) of the AV 100. The steering input 1108 representsa steering angle, e.g., the angle at which the steering control (e.g.,steering wheel, steering angle actuator, or other functionality forcontrolling steering angle) of the AV should be positioned to achievethe desired output 1104.

In an embodiment, the controller 1102 receives feedback that is used inadjusting the inputs provided to the throttle and steering. For example,if the AV 100 encounters a disturbance 1110, such as a hill, themeasured speed 1112 of the AV 100 is lowered below the desired outputspeed. In an embodiment, any measured output 1114 is provided to thecontroller 1102 so that the necessary adjustments are performed, e.g.,based on the differential 1113 between the measured speed and desiredoutput. The measured output 1114 includes measured position 1116,measured velocity 1118, (including speed and heading), measuredacceleration 1120, and other outputs measurable by sensors of the AV100.

In an embodiment, information about the disturbance 1110 is detected inadvance, e.g., by a sensor such as a camera or LiDAR sensor, andprovided to a predictive feedback module 1122. The predictive feedbackmodule 1122 then provides information to the controller 1102 that thecontroller 1102 can use to adjust accordingly. For example, if thesensors of the AV 100 detect (“see”) a hill, this information can beused by the controller 1102 to prepare to engage the throttle at theappropriate time to avoid significant deceleration.

FIG. 12 shows a block diagram 1200 of the inputs, outputs, andcomponents of the controller 1102. The controller 1102 has a speedprofiler 1202 which affects the operation of a throttle/brake controller1204. For example, the speed profiler 1202 instructs the throttle/brakecontroller 1204 to engage acceleration or engage deceleration using thethrottle/brake 1206 depending on, e.g., feedback received by thecontroller 1102 and processed by the speed profiler 1202.

The controller 1102 also has a lateral tracking controller 1208 whichaffects the operation of a steering controller 1210. For example, thelateral tracking controller 1208 instructs the steering controller 1210to adjust the position of the steering angle actuator 1212 depending on,e.g., feedback received by the controller 1102 and processed by thelateral tracking controller 1208.

The controller 1102 receives several inputs used to determine how tocontrol the throttle/brake 1206 and steering angle actuator 1212. Aplanning module 404 provides information used by the controller 1102,for example, to choose a heading when the AV 100 begins operation and todetermine which road segment to traverse when the AV 100 reaches anintersection. A localization module 408 provides information to thecontroller 1102 describing the current location of the AV 100, forexample, so that the controller 1102 can determine if the AV 100 is at alocation expected based on the manner in which the throttle/brake 1206and steering angle actuator 1212 are being controlled. In an embodiment,the controller 1102 receives information from other inputs 1214, e.g.,information received from databases, computer networks, etc.

Localization Based on Predefined Features of the Environment

Returning to FIG. 1 , some areas of the environment 190 includepredefined features 195. In some embodiments, the predefined features195 include the textural pattern of the roadway surface (e.g., groovesor indentations in the roadway surface that vary along the length of theroadway). In some embodiments, the textural pattern is engraved into theroadway surface. In some embodiments, the predefined features 195include predefined location markers (e.g., flags) positioned along aroadway. In some embodiments, the predefined location markers identifylocations along the roadway where the predefined location markers arepositioned.

The predefined features 195 are used in areas of the environment 190where localization using certain sources (e.g., cameras 122, LiDAR 123,and/or GPS) may not be reliable or available. These areas of theenvironment 190 can include areas where GPS signals are not detectableand/or areas with repeating features that are not discernable, such astunnels and bridges. For example, the walls of a tunnel may be repeatingslabs of concrete without discernable features, or the sides of a bridgemay be repeating suspension wires without discernable features.

More specifically, the AV system 120 uses the predefined features 195when the localization data received from other sources fails to satisfyan accuracy criterion. In some embodiments, the accuracy criterion isnot satisfied when localization data is not available or is onlyavailable from one source (e.g., LiDAR only, and not GPS). In someembodiments, the accuracy criterion is not satisfied when differentsources indicate different approximate locations for the AV 100. In someembodiments, two approximate locations are considered to be different ifthe distance between them exceeds a threshold distance. The thresholddistance can be based on an estimated location resolution that accountsfor factors such as sensor resolution, data latency, etc. Stated anotherway, in some embodiments, two estimated locations are considered to bethe same for the purpose of satisfying the accuracy criterion if thedistance between them does not exceed the threshold distance.

The predefined features 195 are used by the AV system 120 to determinean estimated location of the AV 100. In some embodiments, the predefinedfeatures 195 include a predefined location marker that identifies alocation along the roadway where the predefined location marker ispositioned. When the AV system 120 detects the predefined locationmarker, the AV system 120 uses the location as identified by thepredefined location marker to determine an estimated location of the AV100. In some embodiments, the predefined features 195 include texturalpatterns along the roadway surface that are identifiable by the AVsystem 120 (e.g., using high-frequency RADAR). After detecting atextural pattern, the AV system 120 compares the detected texturalpattern to a database of textural patterns with known locations. If thedetected textural pattern matches a respective textural pattern in thedatabase, then the AV system uses the known location of the respectivetextural pattern in the database to provide an estimated location of theAV 100. In some embodiments, the textural patterns in the roadwaysurface are manually etched and have predefined patterns. In someembodiments, the textural patterns in the roadway surface are naturallyoccurring variations in the roadway surface, e.g., that have beenpreviously mapped and stored in the database of textural patterns.

In some embodiments, in addition or alternative to the predefinedfeatures 195, the AV system 120 tracks the motion of the AV 100 toprovide an estimated location of the AV 100 when other sources areunavailable or not reliable (e.g., the other sources fail to satisfy theaccuracy criterion). The AV system 120 detects motion of the AV 100(e.g., with sensors 121 (e.g., IMU), with time and distancemeasurements), and estimates the location of the AV 100 based on thedetected motion. In some embodiments, the AV system 120 tracks themotion of the AV 100 relative to an object with a known location (e.g.,the entrance to a tunnel or bridge).

In areas where the localization data is available and reliable (e.g.,where GPS signals are being received and the roadway has annotatedfeatures detected by LiDAR 123 or cameras 122), no predefined features195 may be included in the area. Instead, the AV system 120 uses thelocalization data from the sources discussed above (e.g., cameras 122,LiDAR 123, and/or GPS). As long as the localization data meets theaccuracy criterion, then the AV system 120 provides an estimatedlocation of the AV 100 using the localization data received from thosesources.

Example Process for Providing an Estimated Location Based on PredefinedFeatures of the Environment

FIG. 13 is a flow chart of an example process 1300 for providing anestimated location of an autonomous vehicle based on predefined featuresof the environment. For convenience, the process 1300 will be describedas being performed by a system of one or more computers located in oneor more locations. For example, the AV system 120 of FIG. 1 (or portionsthereof), appropriately programmed in accordance with thisspecification, can perform the process 1300.

At block 1302, while the system (e.g., AV system 120) is in a firstlocalization mode (e.g., on a mapped roadway with annotated features),the system receives localization data (e.g., from sensors 121 (e.g.,GPS), cameras 122, or LiDAR 123). In some embodiments, receiving thelocalization data while in the first localization mode includesreceiving GPS signals. In some embodiments, receiving the localizationdata while in the first localization mode includes detecting a landmark(e.g., buildings, road signs) in the environment surrounding thevehicle; and determining a location of the landmark.

At block 1304, the system determines whether the localization datasatisfies an accuracy criterion (e.g., number/type of sources providinglocalization data, agreement between sources of the vehicle'sapproximate location (e.g., the sources provide estimated locations thatare within a threshold distance of each other)).

At block 1306, if the accuracy criterion is satisfied (e.g., when thelocalization data is from at least two sources and the sources providethe same approximate location), the system provides an estimatedlocation of a vehicle based at least in part on the localization datareceived at block 1302. In some embodiments, providing the estimatedlocation of the vehicle based at least in part on the receivedlocalization data includes providing the estimated location of thevehicle based at least in part on the received GPS signals. In someembodiments, providing the estimated location of the vehicle based atleast in part on the received localization data includes providing theestimated location of the vehicle based at least in part on the locationof the landmark.

At block 1308, if the accuracy criterion is satisfied (e.g., when thelocalization data is from at least two sources and the sources providethe same approximate location), the system remains in the firstlocalization mode (e.g., the system continues receiving localizationdata as in block 1302).

At block 1310, if the accuracy criterion is not satisfied (e.g., whenthe localization data is from only a single source (e.g., image dataonly without GPS data)), the system switches to a second localizationmode. In some embodiments, the accuracy criterion is not satisfied whenthe received localization data indicates the vehicle is on a roadwaywith repeating features (e.g., when in a tunnel or on a bridge). Forexample, the walls of a tunnel may be repeating slabs of concretewithout discernable features, or the sides of a bridge may be repeatingsuspension wires without discernable features, which may prevent asensor from being able to distinguish one location from another, andthus inhibiting the system from identifying the particular locationbased on data from that sensor.

At block 1312, while in the second localization mode, the system detectsone or more predefined features (e.g., 195) (e.g., road surface,pre-installed markers) of the environment surrounding the vehicle (e.g.,with cameras 122, LiDAR 123, RADAR, or ultrasonic sensors). In someembodiments, the one or more predefined features include a texturalpattern of a roadway surface (e.g., grooves or indentations in theroadway surface that vary along the length of the roadway). In someembodiments, the system detects the textural pattern of the roadwaysurface using a high-frequency radar device. In some embodiments, thetextural pattern of the roadway surface includes predetermined patternsengraved into the roadway surface. In some embodiments, the one or morepredefined features include predefined location markers (e.g., flags)positioned along a roadway. In some embodiments, a respective predefinedlocation marker identifies a location along the roadway where therespective predefined location marker is positioned (e.g., the locationmarker includes an indication (symbol, number, etc.) of its locationthat distinguishes it from other markers in different locations). Insome embodiments, while in the second localization mode, the systemdetects motion of the vehicle (e.g., with sensors 121 (e.g., IMU), timeand distance measurements).

At block 1314, while in the second localization mode, the systemprovides the estimated location of the vehicle based on the one or morepredefined features of the environment surrounding the vehicle (e.g.,without localization data from the first localization mode). In someembodiments, providing the estimated location of the vehicle based onthe one or more predefined features of the environment surrounding thevehicle includes providing the estimated location of the vehicle withoutusing GPS signals. In some embodiments, providing the estimated locationof the vehicle based on the one or more predefined features of theenvironment surrounding the vehicle includes providing the estimatedlocation of the vehicle based on detected motion of the vehicle (e.g.,with sensors 121 (e.g., IMU), time and distance measurements) and theone or more predefined features of the environment surrounding thevehicle. In some embodiments, providing the estimated location of thevehicle includes determining, based on the detected motion of thevehicle (e.g., with sensors 121 (e.g., IMU), time and distancemeasurements), a relative location of the vehicle with respect to astructure with a known location (e.g., entrance to tunnel or bridge).

In the foregoing description, embodiments have been described withreference to numerous specific details that may vary from implementationto implementation. The description and drawings are, accordingly, to beregarded in an illustrative rather than a restrictive sense. The soleand exclusive indicator of the scope of the claims, and what is intendedby the applicants to be the scope of the claims, is the literal andequivalent scope of the set of claims that issue from this application,in the specific form in which such claims issue, including anysubsequent correction. Any definitions expressly set forth herein forterms contained in such claims shall govern the meaning of such terms asused in the claims. In addition, when we use the term “furthercomprising,” in the foregoing description or following claims, whatfollows this phrase can be an additional step or entity, or asub-step/sub-entity of a previously-recited step or entity.

What is claimed is:
 1. A system comprising: one or more computerprocessors; and one or more non-transitory media storing instructionswhich, when executed by the one or more computer processors, causeperformance of operations comprising: receiving first localization dataof a vehicle and second localization data of the vehicle at a firsttime, wherein the first localization data is from a first source and thesecond localization data is from a second source different from thefirst source; determining a first difference between the firstlocalization data and the second localization data received at the firsttime; based on determining that the first difference between the firstlocalization data and the second localization data satisfies an accuracycriterion, providing a first estimated location of the vehicle based onthe first localization data and the second localization data; receivingfirst localization data of the vehicle and second localization data ofthe vehicle at a second time; determining a second difference betweenthe first localization data and the second localization data received atthe second time; determining that the second difference between thefirst localization data and the second localization data at the secondtime does not satisfy the accuracy criterion; and based on determiningthat the second difference between the first localization data and thesecond localization data at the second time does not satisfy theaccuracy criterion: detecting, using one or more images of anenvironment surrounding the vehicle, one or more predefined features ofthe environment; providing a second estimated location of the vehiclebased on the one or more predefined features of the environment; andcausing the vehicle to navigate based on the second estimated locationof the vehicle.
 2. The system of claim 1, wherein providing the secondestimated location of the vehicle based on the one or more predefinedfeatures of the environment includes providing the second estimatedlocation of the vehicle without using GPS signals.
 3. The system ofclaim 1, wherein receiving the first localization data and the secondlocalization data at the first time includes: detecting a landmark inthe environment; and determining a location of the landmark, whereinproviding the first estimated location of the vehicle based at least inpart on the first localization data includes providing the firstestimated location of the vehicle based at least in part on the locationof the landmark.
 4. The system of claim 1, wherein the accuracycriterion is also not satisfied when the first localization dataindicates the vehicle is on a roadway with repeating features.
 5. Thesystem of claim 1, wherein the one or more predefined features include atextural pattern of a roadway surface.
 6. The system of claim 5, whereinthe instructions cause performance of operations comprising: detectingthe textural pattern of the roadway surface using a high-frequency radardevice.
 7. The system of claim 5, wherein the textural pattern of theroadway surface includes predetermined patterns engraved into theroadway surface.
 8. The system of claim 1, wherein the one or morepredefined features include predefined location markers positioned alonga roadway.
 9. The system of claim 8, wherein a respective predefinedlocation marker identifies a location along the roadway where therespective predefined location marker is positioned.
 10. The system ofclaim 1, wherein the instructions cause performance of operationscomprising: detecting motion of the vehicle, wherein providing thesecond estimated location of the vehicle based on the one or morepredefined features of the environment includes providing the secondestimated location of the vehicle based on the detected motion of thevehicle and the one or more predefined features of the environment. 11.The system of claim 10, wherein providing the second estimated locationof the vehicle includes determining, based on the detected motion of thevehicle, a relative location of the vehicle with respect to a structurewith a known location.
 12. A method, comprising: receiving firstlocalization data of a vehicle and second localization data of thevehicle at a first time, wherein the first localization data is from afirst source and the second localization data is from a second sourcedifferent from the first source; determining a first difference betweenthe first localization data and the second localization data received atthe first time; based on determining that the first difference betweenthe first localization data and the second localization data satisfiesan accuracy criterion, providing a first estimated location of thevehicle based on the first localization data and the second localizationdata; receiving first localization data of the vehicle and secondlocalization data of the vehicle at a second time; determining a seconddifference between the first localization data and the secondlocalization data received at the second time; determining that thesecond difference between the first localization data and the secondlocalization data at the second time does not satisfy the accuracycriterion; and based on determining that the second difference betweenthe first localization data and the second localization data at thesecond time does not satisfy the accuracy criterion: detecting, usingone or more images of an environment surrounding the vehicle, one ormore predefined features of the environment; providing a secondestimated location of the vehicle based on the one or more predefinedfeatures of the environment; and causing the vehicle to navigate basedon the second estimated location of the vehicle.
 13. The method of claim12, wherein the accuracy criterion is also not satisfied when the firstlocalization data indicates the vehicle is on a roadway with repeatingfeatures.
 14. The method of claim 12, wherein the one or more predefinedfeatures include a textural pattern of a roadway surface.
 15. The methodof claim 12, wherein the one or more predefined features includepredefined location markers positioned along a roadway.
 16. The methodof claim 12, further comprising: detecting motion of the vehicle,wherein providing the second estimated location of the vehicle based onthe one or more predefined features of the environment includesproviding the second estimated location of the vehicle based on thedetected motion of the vehicle and the one or more predefined featuresof the environment.
 17. The method of claim 16, wherein providing thesecond estimated location of the vehicle includes determining, based onthe detected motion of the vehicle, a relative location of the vehiclewith respect to a structure with a known location.
 18. One or morenon-transitory media storing instructions which, when executed by one ormore computing devices, cause performance of operations comprising:receiving first localization data of a vehicle and second localizationdata of the vehicle at a first time, wherein the first localization datais from a first source and the second localization data is from a secondsource different from the first source; determining a first differencebetween the first localization data and the second localization datareceived at the first time; based on determining that the firstdifference between the first localization data and the secondlocalization data satisfies an accuracy criterion, providing a firstestimated location of the vehicle based on the first localization dataand the second localization data; receiving first localization data ofthe vehicle and second localization data of the vehicle at a secondtime; determining a second difference between the first localizationdata and the second localization data received at the second time;determining that the second difference between the first localizationdata and the second localization data at the second time does notsatisfy the accuracy criterion; and based on determining that the seconddifference between the first localization data and the secondlocalization data at the second time does not satisfy the accuracycriterion: detecting, using one or more images of an environmentsurrounding the vehicle, one or more predefined features of theenvironment; providing a second estimated location of the vehicle basedon the one or more predefined features of the environment; and causingthe vehicle to navigate based on the second estimated location of thevehicle.